Unmixing of hyperspectral data for mineral detection using a hybrid method, Sar Chah-e Shur, Iran
01 Oct 2020-Arabian Journal of Geosciences (Springer International Publishing)-Vol. 13, Iss: 19, pp 1-17
TL;DR: This study aims to detect indicative minerals by spectral unmixing of the Hyperion and HyMap datasets in the Sar Chah-e Shur area using a series of hyperspectral processing algorithms to determine mineral endmembers and their abundances.
Abstract: This study aims to detect indicative minerals by spectral unmixing of the Hyperion and HyMap datasets in the Sar Chah-e Shur area. The mineral endmembers and their abundances were therefore determined using a series of hyperspectral processing algorithms. The virtual dimensionality methods including principal component analysis (PCA), minimum noise fraction (MNF), singular valued decomposition (SVD), Harsanyi-Farrand-Chang (HFC)/ (NWHFC), and Hyperspectral signal subspace identification by minimum error (HySime) were applied to estimate the number of endmembers. Five pure pixel-based methods including pixel purity index (PPI), sequential maximum angle convex cone (SMACC), simplex growing algorithm (SGA), N-FINDR, and vertex component analysis (VCA) were then applied for extracting the spectra of endmembers. Clay, serpentine, mica, and zeolite group minerals were identified which are consistent with the geological investigations in the region. The detected minerals were then mapped by the fully constrained least square (FCLS) method. The functionality of the methods and their performances on HyMap and Hyperion data were surveyed by several criteria including the number of recognized endmembers, the matching score of extracted endmembers with the reference spectrum, the agreement of the estimated abundances maps with the relevant lithological units on the geological map, and the average reconstruction error (ARE). Two hybrid maps were generated by combining individual methods that were found highly consistent with the geological map. The XRD analysis of three chips rock samples of two indicative lithological units was used to additionally check the efficiency of the applied methods.
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TL;DR: In this paper, the authors discussed the possibility of using hyperspectral technology to detect total nitrogen content (TN) in soil, and analyzed six spectral data preprocessing methods and five modeling methods: partial least squares (PLS), back-propagation (BP) neural network, radial basis function (RBF), extreme learning machine (ELM), and support vector regression (SVR) with evaluation index R2 and RMSE.
4 citations
TL;DR: In this paper , the authors discussed the possibility of using hyperspectral technology to detect total nitrogen content (TN) in soil, analyzed six spectral data preprocessing methods and five modeling methods: partial least squares (PLS), back-propagation (BP) neural network, radial basis function (RBF) neural networks, extreme learning machine (ELM), and support vector regression (SVR) with evaluation index R2 and RMSE.
2 citations
TL;DR: In this paper, the MTMF was applied on the Hyperion data to determine the distributions of these alteration minerals in the study area, which are belonging to argillic, sericitic, propylitic, and FeOx (iron oxide) hydrothermal alteration.
Abstract: The Kosedag region, is located in Central-Eastern Anatolia, contains a lot of base metals (Pb–Zn, Cu) and Au occurrences. The region is explored by numerous mining companies and MTA. In this research, hydrothermal alteration mapping by hyperspectral Hyperion satellite data was carried out to contribute to these explorations in a part of the region. Hydrothermal alteration is one of the initial steps in the exploration of such metallic occurrences. This study area was chosen to test the accuracy of the hyperspectral data results with those of field and laboratory study results. The in situ alteration map was prepared during field surveys and numerous samples analyses. These samples were investigated by microscopy and XRD examinations. The minerals which are belonging to argillic, sericitic, propylitic, and FeOx (iron oxide) hydrothermal alterations were determined. These minerals were checked and confirmed on the surfaces of altered samples by using ASD fieldspec 4 hi-res. The mixture tuned matched filtering (MTMF), which is one of the widely used spectral classification methods, was applied on the Hyperion data to determine the distributions of these alteration minerals in the study area. The results have shown that the comparison of field and laboratory studies and MTMF results coincided with an overall accuracy of over 86% and a kappa coefficient of 0.80. The alteration map has been generated using the MTMF method, has been the first hyperspectral research of the hydrothermal alteration mineralogy in the region. Consequently, the generated map can be used as a basic alteration map during exploration studies of hydrothermal base metal mineralizations in the region.
2 citations
TL;DR: In this article, the authors applied the Modified Spectral Angle Mapper MSAM and continuum-removal methods to reflectance data obtained in the shortwave infrared regions by the airborne hyperspectral sensor, HyMap, to delin...
Abstract: We applied the Modified Spectral Angle Mapper MSAM and continuum-removal methods to reflectance data obtained in the shortwave infrared regions by the airborne hyperspectral sensor, HyMap, to delin...
1 citations
TL;DR: In this paper , the authors provide an updated overview of the approaches used, analyzing the papers that were published in 2022, 2021, and 2020, and a dated overview, analyzing some papers published not only in 2011 and 2010, but also in 1996 and 1995.
Abstract: The pixels of remote images often contain more than one distinct material (mixed pixels), and so their spectra are characterized by a mixture of spectral signals. Since 1971, a shared effort has enabled the development of techniques for retrieving information from mixed pixels. The most analyzed, implemented, and employed procedure is spectral unmixing. Among the extensive literature on the spectral unmixing, nineteen reviews were identified, and each highlighted the many shortcomings of spatial validation. Although an overview of the approaches used to spatially validate could be very helpful in overcoming its shortcomings, a review of them was never provided. Therefore, this systematic review provides an updated overview of the approaches used, analyzing the papers that were published in 2022, 2021, and 2020, and a dated overview, analyzing the papers that were published not only in 2011 and 2010, but also in 1996 and 1995. The key criterion is that the results of the spectral unmixing were spatially validated. The Web of Science and Scopus databases were searched, using all the names that were assigned to spectral unmixing as keywords. A total of 454 eligible papers were included in this systematic review. Their analysis revealed that six key issues in spatial validation were considered and differently addressed: the number of validated endmembers; sample sizes and sampling designs of the reference data; sources of the reference data; the creation of reference fractional abundance maps; the validation of the reference data with other reference data; the minimization and evaluation of the errors in co-localization and spatial resampling. Since addressing these key issues enabled the authors to overcome some of the shortcomings of spatial validation, it is recommended that all these key issues be addressed together. However, few authors addressed all the key issues together, and many authors did not specify the spatial validation approach used or did not adequately explain the methods employed.
References
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Book•
01 Jan 2008
TL;DR: In this paper, the authors present an introduction to quantitative evaluation of satellite and aircraft derived from remotely retrieved data, without detailed mathematical treatment of computer based algorithms, but in a manner conductive to an understanding of their capabilities and limitations.
Abstract: From the Publisher:
The book provides the non-specialist with an introduction to quantitative evaluation of satellite and aircraft derived from remotely retrieved data. Each chapter covers the pros and cons of digital remotely sensed data, without detailed mathematical treatment of computer based algorithms, but in a manner conductive to an understanding of their capabilities and limitations. Problems conclude each chapter.
3,532 citations
TL;DR: The Center for the Study of Earth from Space (CSES) at the University of Colorado, Boulder, has developed a prototype interactive software system called the Spectral Image Processing System (SIPS) using IDL (the Interactive Data Language) on UNIX-based workstations to develop operational techniques for quantitative analysis of imaging spectrometer data.
2,686 citations
TL;DR: A new method for unsupervised endmember extraction from hyperspectral data, termed vertex component analysis (VCA), which competes with state-of-the-art methods, with a computational complexity between one and two orders of magnitude lower than the best available method.
Abstract: Given a set of mixed spectral (multispectral or hyperspectral) vectors, linear spectral mixture analysis, or linear unmixing, aims at estimating the number of reference substances, also called endmembers, their spectral signatures, and their abundance fractions. This paper presents a new method for unsupervised endmember extraction from hyperspectral data, termed vertex component analysis (VCA). The algorithm exploits two facts: (1) the endmembers are the vertices of a simplex and (2) the affine transformation of a simplex is also a simplex. In a series of experiments using simulated and real data, the VCA algorithm competes with state-of-the-art methods, with a computational complexity between one and two orders of magnitude lower than the best available method.
2,422 citations
TL;DR: This paper presents an overview of un Mixing methods from the time of Keshava and Mustard's unmixing tutorial to the present, including Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixed algorithms.
Abstract: Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.
2,373 citations
"Unmixing of hyperspectral data for ..." refers background or methods in this paper
...By using this method, four mineral endmembers for Hyperion and seven for HyMap were identified (Bioucas-Dias et al. 2012; Douglas et al. 2018; Nascimento and Dias 2005; Song et al. 2020)....
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...This process is iterated until a projection derives an existing endmember within the cone or until the defined VD (Aggarwal and Garg 2015; Bioucas-Dias et al. 2012; Gruninger et al. 2004)....
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...implemented unmixing algorithms in this research are therefore based on Linear mixture model (LMM) too (Bioucas-Dias et al. 2012; Cao et al. 2018; Keshava and Mustard 2002)....
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...However, they do not satisfy the ASC and ANC constraints....
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...Therefore, it is necessary to decompose these mixtures through the so-called spectral unmixing methods (Bioucas-Dias et al. 2012; Heylen et al. 2014; Keshava and Mustard 2002)....
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Journal Article•
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TL;DR: The outputs of spectral unmixing, endmember, and abundance estimates are important for identifying the material composition of mixtures and the applicability of models and techniques is highly dependent on the variety of circumstances and factors that give rise to mixed pixels.
Abstract: Spectral unmixing using hyperspectral data represents a significant step in the evolution of remote decompositional analysis that began with multispectral sensing. It is a consequence of collecting data in greater and greater quantities and the desire to extract more detailed information about the material composition of surfaces. Linear mixing is the key assumption that has permitted well-known algorithms to be adapted to the unmixing problem. In fact, the resemblance of the linear mixing model to system models in other areas has permitted a significant legacy of algorithms from a wide range of applications to be adapted to unmixing. However, it is still unclear whether the assumption of linearity is sufficient to model the mixing process in every application of interest. It is clear, however, that the applicability of models and techniques is highly dependent on the variety of circumstances and factors that give rise to mixed pixels. The outputs of spectral unmixing, endmember, and abundance estimates are important for identifying the material composition of mixtures.
1,917 citations
"Unmixing of hyperspectral data for ..." refers methods in this paper
...The implemented unmixing algorithms in this research are therefore based on Linear mixture model (LMM) too (Bioucas-Dias et al. 2012; Cao et al. 2018; Keshava and Mustard 2002)....
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...The nonlinear model, on the other hand, is used when the mixing scale is microscopic or materials are mixed intrinsically (Bioucas-Dias et al. 2012; Heylen et al. 2014; Keshava and Mustard 2002)....
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...Therefore, it is necessary to decompose these mixtures through the so-called spectral unmixing methods (Bioucas-Dias et al. 2012; Heylen et al. 2014; Keshava and Mustard 2002)....
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